| Autor: |
Gupta, Manish, Kumar, Naresh, Gupta, Neha, Zaguia, Atef |
| Předmět: |
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| Zdroj: |
Soft Computing - A Fusion of Foundations, Methodologies & Applications; Aug2022, Vol. 26 Issue 16, p8025-8036, 12p |
| Abstrakt: |
With the recent advancement in the medical diagnostic tools, multi-modality medical images are extensively utilized as a lifesaving tool. An efficient fusion of medical images can improve the performance of various medical diagnostic tools. But, gathering of all modalities for a given patient is defined as an ill-posed problem as medical images suffer from poor visibility and frequent patient dropout. Therefore, in this paper, an efficient multi-modality image fusion model is proposed to fuse multi-modality medical images. To tune the hyper-parameters of the proposed model, a multi-objective differential evolution is used. The fusion factor and edge strength metrics are utilized to form a multi-objective fitness function. Performance of the proposed model is compared with nine competitive models over fifteen benchmark images. Performance analyses reveal that the proposed model outperforms the competitive fusion models. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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